[2B1-GS-2] Machine learning: Text mining

[2B1-GS-2-04] Analysis of Subjective Differences among Raters in the Estimation of Product Image Values Based on Pairwise Comparison Deep Neural Network

〇Ayako Yamagiwa¹, Masayuki Goto¹ (1. Waseda University)

Keywords:Product Analysis, Estimation of Product Image Evaluation, Subjective Evaluation, Pairwise Comparison Deep Neural Network

In creating product maps, which are effective as a method of product analysis, the authors have shown that using a pairwise comparison DNN to estimate human evaluations significantly improves the efficiency of map creation relative to the number of data acquisitions compared to conventional creation methods. Specifically, assuming that the ratio of the evaluation values of two product image data to be compared can be obtained in a one-to-one comparison, a deep learning model can be trained using a realistic number of comparison results, and the estimated values can be used to estimate the evaluation values for an arbitrary evaluation axis for all product images. Here, subjective axes such as "cute" and "gorgeous" can be considered appropriate for the product map to be analyzed. When such personal axes are used, differences due to the subjectivity of the evaluators are expected to occur in the product image evaluation values. Therefore, in this study, we analyze how the subjectivity of the evaluators affects the estimation results of the DNN model by using a pair-wise comparison between multiple evaluators. Furthermore, we show that it is possible to analyze the similarity of evaluators' preferences by using the parameters of the deep learning model for pair-wise comparison.